Inhibiting mechanism of small molecule toward the p53‐MDM2 interaction: A molecular dynamic exploration

Abstract

Disruption of the p53-MDM2 interaction has been an efficient strategy to renew the function of wild-type p53. In this work, molecular dynamic simulations, molecular mechanics-generalized Born surface area method, and principal component analysis were combined to probe interaction mechanism of inhibitors 2TZ, 2U0, 2U1, 2U5, 2U6, and 2U7 with MDM2. The rank of our current predicted binding free energies is in agreement with that of the experimental values. The results demonstrate that the introductions of thiazole and pyridine rings into 2TZ as well as the change in the orientation of inhibitors lead to the increase in the polar interactions of 2U0, 2U1, 2U5, 2U6, and 2U7 with MDM2 relative to 2TZ. The information derived from principal component analysis suggests that inhibitor bindings produce significant effect on the binding cleft of MDM2 and make the binding cleft wider and bigger so as to accommodate different type inhibitors. This study is looked forward to contributing theoretical hints for designs of potent inhibitors targeting the p53-MDM2 interaction.

Binding Selectivity of Inhibitors toward Bromodomains BAZ2A and BAZ2B Uncovered by Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations

“… 33 , 46 Moreover, various works have been involved in successful insights into binding selectivity of small compounds toward homologous receptors with very similar sequence. 47 − 52 However, the conformations of receptors sampled by cMD simulations are possibly trapped at a local minimum energy well, 53 which will generate an insufficient structural ensemble and affect statistical rationality. To relieve this sampling issue in cMD simulations, multiple short molecular dynamics simulations (MSMDSs) with various initial conformations are proposed so as to obtain better sampling efficiency than a single long trajectory.…”

“…With rapid development of calculational technology and computer hardware, classical molecular dynamics (cMD) simulations − and binding affinity computations − increasingly play significant roles in unveiling molecular mechanism and conformational transformations of receptors. In calculations of binding affinity, the entropic computation is highly changing, Duan et al proposed a more efficient method of entropic calculations, namely interaction entropy, which not only obtains rational results but also saves computational time. , Moreover, various works have been involved in successful insights into binding selectivity of small compounds toward homologous receptors with very similar sequence. − However, the conformations of receptors sampled by cMD simulations are possibly trapped at a local minimum energy well, which will generate an insufficient structural ensemble and affect statistical rationality. To relieve this sampling issue in cMD simulations, multiple short molecular dynamics simulations (MSMDSs) with various initial conformations are proposed so as to obtain better sampling efficiency than a single long trajectory. , Recently, different works verify that MSMDSs can indeed obtain rational conformational samplings of receptors, moreover MSMDSs have been extensively applied to uncover conformational transformations of receptors, binding selectivity, drug resistance, etc. − In the present work, three inhibitors, namely D8Q, D9T, and UO1 were chosen to study their binding selectivity toward BAZ2A/B and decipher selectivity-dependent molecular mechanism.…”

Extraction of molecular features for the drug discovery targeting protein‐protein interaction of Helicobacter pylori CagA and tumor suppressor protein ASSP2

“…However, mutational analyses of PPI interfaces revealed that the binding affinity is not evenly distributed across the binding interfaces, but instead contributed by the small patch of amino acid residues know as hot spot . Most of the hot spots are enough compact in size to be filled by the peptide or small molecules . Identification of hot spot residues at the interface by experiments is time consuming and costly.…”

“…[25][26][27][28] Most of the hot spots are enough compact in size to be filled by the peptide or small molecules. [29][30][31] Identification of hot spot residues at the interface by experiments is time consuming and costly. Therefore, several computational techniques, such as machine learning approach, molecular docking, and computational alanine scanning, have been developed with high accuracy to predict hot spot residues.…”